A Systematic Review of User Acceptance in Industrial Augmented Reality

Abstract

In the industrial work context, Augmented Reality (AR) can support work processes and employees’ cognitive relief through the location-specific and context-related superimposition of real objects with virtual information. The AR-based support of industrial work processes ranges over product development, manufacturing, assembly, maintenance, and training. In all these areas, numerous location-based AR support functions are being prototypically implemented, aiming to improve work efficiency, communication in mobile work situations, or employee qualification in the work process. In contrast to the increasing number of developed AR solutions in recent years, there is no widespread use of these solutions in industrial practice. AR systems’ successful introduction is closely related to user acceptance, which has not been comprehensively considered over the system development process. In addition to improving AR hardware ergonomic features, usability or user interface design play an essential role in user acceptance. Particularly in the context of employee qualification, increasing employee engagement can be named as a success factor. Previous user studies of industrial AR systems only include individual user acceptance aspects. The use of game elements has not been widely addressed in connection with manual tasks in production environments, including AR-based assistance systems. This paper aims to examine user acceptance of industrial AR systems and the relevant factors for investigating user acceptance, e.g., ease of use or enjoyment, based on a systematic literature review. An analysis of existing review articles on industrial AR systems elaborates the current state of the art and identifies the research gap. This review of 109 scientific articles from 2011 to 2020 provides an overview of the current state of research on the inclusion of user acceptance in industrial AR systems. The identified papers from the scientific databases, Scopus, Web of Science, IEEE Xplore Digital Library, ACM Digital Library, and Science Direct, are evaluated for their relevance and selected for further analysis based on inclusion and exclusion criteria, e.g., year of publication. This review presents the current challenges regarding user acceptance of industrial AR systems and future possibilities for the comprehensive integration of user acceptance factors into the development, evaluation, and implementation process.